Artificial Intelligence

11,900.00 (Inc. GST)

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This foundational Artificial Intelligence (AI) course offers an in-depth exploration of the fundamental concepts, applications, and techniques in AI. It covers various branches of AI, including machine learning (ML), deep learning (DL), and computer vision (CV). The course takes students through the history and evolution of AI, types of AI systems, and key algorithms such as supervised and unsupervised learning. Topics also include Artificial Neural Networks (ANN), Deep Layer Neural Networks (DNN), and Convolutional Neural Networks (CNN). The course concludes by discussing the future of AI, its societal impacts, and ethical concerns.

Course Features:

  • Comprehensive exploration of AI and its subfields, including ML, DL, and CV.
  • Learn and implement key AI algorithms: supervised, unsupervised learning, ANN, DNN, and CNN.
  • Practical experience in image classification and computer vision tasks.
  • Focus on understanding deep learning architectures like feedforward and convolutional neural networks.
  • Explore future trends in AI and its ethical and societal implications.

Prerequisites:

  • Basic knowledge of Python programming is helpful, but not mandatory for this course.

Key Learning Outcomes:

By the end of this course, participants will be able to:

  • Define AI and understand its history, types, and systems.
  • Apply machine learning algorithms, including supervised, unsupervised, and deep learning techniques.
  • Preprocess and represent data for ML and DL tasks.
  • Work with image data in computer vision tasks like image classification.
  • Use deep learning techniques (ANN, DNN, CNN) for complex computer vision tasks.
  • Analyze AI’s future trends and understand its impact on society, including ethical and safety considerations.
  • Combine theoretical knowledge and practical skills to form a solid understanding of AI.

Target Audience:

  • Individuals looking to pursue a career in AI, ML, DL, Data Science, or Data Analytics.
  • Anyone interested in understanding the current and future landscape of AI and its real-world applications.

Test & Evaluation:

  • Participants must complete all assignments for effective learning.
  • A final assessment will be conducted at the end of the program to evaluate participants’ progress.

Certification:

  • Successful participants will receive a Certificate of Completion.
  • A Project Letter will be awarded upon the successful completion of the project.
  • Students who leave the course midway or do not complete it will not receive any certification.

Delivery Mode & Duration:

  • Mode: Online Live Sessions
  • Duration: 120 Hours (60 Hours of Online Live Sessions + 60 Hours of Assignments)

Additional information

Centre for Summer Training

IIT Kanpur Campus, Online Live

Batch Date

Batch 1: 19th May 2025 – 25th June 2025, Batch 2: 17th June 2025 – 22nd July 2025

Curriculum

Module 01 – Introduction of Artificial Intelligence

  • Introduction of Artificial Intelligence
  • Terminologies of Artificial Intelligence
  • Components of Artificial Intelligence – ML & DL
  • Difference between AI, ML, Deep Learning
  • Introduction to Machine Learning
  • History and Evolution of AI
  • Find out where AI is applied in Technology and Science.
  • Difference between Traditional Programming and ML Programming

Module 02 – Steps of AI/ML Implementations

  • Types of Machine Learning
  • Labelled Data and Unlabeled Data
  • Concept of Supervised Machine Learning
  • Concept of Unsupervised Machine Learning
  • Steps of Machine Learning
  • Concept of Collecting the historic training Data for ML
  • Concept of Preprocess data for Machine Learning
  • Concept of Train the ML model
  • Concept of Test the ML Algorithm
  • Concept of using the ML Algorithm

Module 03 – Data Collection for Machine Learning

  • Introduction
  • Types of Data collection- Offline Data and Online Data
  • Practical implementations of Reading the offline dataset using Numpy
  • Practical implementations of Reading the online dataset using Numpy
  • Practical implementations of Reading the offline dataset using Pandas
  • Practical implementations of Reading the online iris dataset using Pandas

Module 04 – Concept of Supervise & Unsupervised Machine Learning

  • Introduction
  • Types of Machine Learning
  • Labelled Data and Unlabeled Data
  • Concept of Supervised Machine Learning
  • Concept of Unsupervised Machine Learning
  • Regression and Classification
  • Linear Regression and Logistic Regression

Module 05 – Data Visualization for Machine Learning using Matplotlib

  • Introduction
  • Concept of Univariate plots
  • Univariate Histogram Plots.
  • Univariate Density Plots.
  • Univariate Box and Whisker Plots.
  • Concept of Multivariate plots
  • Correlation Matrix Plot
  • Scatter Matrix Plot

Module 06 – Practical implementation of Supervised ML Algorithm

  • Introduction
  • Implementation Foundation of Supervised Machine Learning Algorithms
  • Regression and Classification
  • Linear Regression and Logistic Regression
  • Practical implementations of Supervised ML Algorithms- Linear Regression
  • Practical implementations of Supervised ML Algorithms- Logistic Regression
  • Concept of Sigmoid Function
  • k-NN Algorithm
  • Naive Bayes Classifiers
  • Decision trees etc.

Module 07 – Practical implementation of Unsupervised Machine Learning 

  • Introduction
  • Concepts and Steps of Unsupervised Machine Learning Algorithm 
  • Concept of Clustering,
  • Practical implementations of Machine Learning Unsupervised Algorithms
  • K-Means Clustering.

Module 08 – Prepare Data for ML using Data Transformation Methods

  • Introduction
  • Need for Data Pre-processing
  • Data Transforms Steps
  • Types of Data Transformation Methods
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data

Module 09 – Feature Selection for Machine Learning

  • Introduction
  • Feature Selection
  • Univariate Feature Selection
  • Recursive Feature Elimination
  • Principal Component Analysis
  • Feature Selection based on Importance.

Module 10 – Data Resampling Methods for Evaluation of ML Models

  • Introduction
  • Evaluate Machine Learning Algorithms
  • Split into Train and Test Sets
  • K-fold Cross Validation
  • Leave One Out Cross Validation
  • Repeated Random Test-Train Splits
  • What Techniques to Use When

Module 11 – Machine Learning Algorithm Performance Evaluation Metrics

  • Introduction
  • Algorithm Evaluation Metrics
  • Logistic Regression Algorithm Performance Evaluation Metrics
  • Classification Accuracy (Default).
  • Logarithmic Loss.
  • Area Under ROC Curve (AUC).
  • Confusion Matrix.
  • Classification Report.
  • Linear Regression Algorithm Performance Evaluation Metrics
  • Mean Absolute Error.
  • Mean Squared Error.
  • R2 Error

Module 12 – Spot-Check Machine Learning Algorithms

  • Concept of Algorithm Spot-Checking
  • Algorithms Overview
  • Linear Machine Learning Algorithms Spot-check
  • Nonlinear Machine Learning Algorithms Spot-check

Module 13 – Introduction to Deep Learning

  • A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning

Module 14 – Introduction to Neural Networks

  • How Deep Learning Works?
  • Introduction to Neural Networks
  • Neural Network Architecture
  • The Neuron
  • Training a Perceptron
  • Concept of Gradient Descent
  • Stochastic Gradient Descent (SDG)
  • Activation Functions
  • Neural Network Layers

Module 15 – Deep dive into ANN with Tensor Flow

  • Understand limitations of a Single Perceptron
  • Deepening the network
  • Tensor Flow code-basics
  • Tensor flow data types
  • CPU vs GPU vs TPU
  • Tensor flow methods
  • Overfitting and Regularization
  • Debugging Neural Networks
  • Visualizing NN using Tensor Flow
  • The MNIST Dataset
  • Coding MNIST NN
  • Linear Regression example revisited.
  • Generalization, Overfitting, Under fitting

Module 16 – Computer Vision 

  • Introduction to image processing and computer vision
  • Convolutional features for visual recognition
  • Object detection
  • Image classification

Module 17 – Introduction of Convolutional Neural Networks (CNN)

  • Introduction
  • Images and Pixels
  • How humans recognize images
  • Convolutional Neural Networks

Module 18 – Architecture of Convolutional Neural Networks (CNN)

  • ConvNet Architecture
  • Strides and Zero Padding
  • Max Pooling and ReLU activations
  • Dropout
  • Coding Deep ConvNets demo

Module 19 – Keras API

  • Keras API
  • How to compose Models using Keras
  • Sequential Composition
  • Neural Network Layers with Keras & Tensor Flow

Module 20 – Conclusion and Future of Artificial Intelligence

  • Summary of Artificial Intelligence concepts and techniques
  • Artificial Intelligence trends and future developments
  • Artificial Intelligence and society
  • Artificial Intelligence ethics and safety.

Module 21: Capstone Project

  • Participants will work on a project involving AI algorithms, such as image classification using CNN.
  • The project will demonstrate the application of AI techniques learned during the course.

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